The spring-loaded genome: nucleosome redistributions are widespread, transient, and DNA-directed.

Nucleosome occupancy plays a key role in regulating access to eukaryotic genomes. Although various chromatin regulatory complexes are known to regulate nucleosome occupancy, the role of DNA sequence in this regulation remains unclear, particularly in mammals. To address this problem, we measured nucleosome distribution at high temporal resolution in human cells at hundreds of genes during the reactivation of Kaposi's sarcoma-associated herpesvirus (KSHV). We show that nucleosome redistribution peaks at 24 h post-KSHV reactivation and that the nucleosomal redistributions are widespread and transient. To clarify the role of DNA sequence in these nucleosomal redistributions, we compared the genes with altered nucleosome distribution to a sequence-based computer model and in vitro-assembled nucleosomes. We demonstrate that both the predicted model and the assembled nucleosome distributions are concordant with the majority of nucleosome redistributions at 24 h post-KSHV reactivation. We suggest a model in which loci are held in an unfavorable chromatin architecture and "spring" to a transient intermediate state directed by DNA sequence information. We propose that DNA sequence plays a more considerable role in the regulation of nucleosome positions than was previously appreciated. The surprising findings that nucleosome redistributions are widespread, transient, and DNA-directed shift the current perspective regarding regulation of nucleosome distribution in humans.

Wavelet analysis of variance box plot.

Functional box plots satisfy two needs; visualization of functional data, and the calculation of important box plot statistics. Data visualization illuminates key characteristics of functional sets missed by statistical tests and summary statistics. The calculation of box plot statistics for functional sets permits a novel comparison more suited to functional data. The functional box plot uses a depth method to visualize and rank smooth functional curves in terms of a mean, box, whiskers, and outliers. The functional box plot improves upon other classic functional data analysis tools such as functional principal components and discriminant analysis for outlier detection. This research adds wavelet analysis as a generating mechanism along with depth for functional box plots to visualize functional data and calculate relevant statistics. The wavelet analysis of variance box plot tool gives competitive error rates in Gaussian test cases with magnitude outliers, and outperforms the functional box plot, for Gaussian test cases with shape outliers. Further, we show wavelet analysis is well suited at approximating irregular and noisy functional data and show the enhanced capability of WANOVA box plots to classify shape outliers which follow a different pattern than other functional data for both simulated and real data instances.